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Year: 2017
Exploring the microbiome of healthy and diseased peri-implant sites usingIllumina sequencing
Sanz-Martin, Ignacio <javascript:contributorCitation( ’Sanz-Martin, Ignacio’ );>; Doolittle-Hall, Janet<javascript:contributorCitation( ’Doolittle-Hall, Janet’ );>; Teles, Ricardo P
<javascript:contributorCitation( ’Teles, Ricardo P’ );>; Patel, Michele <javascript:contributorCitation(’Patel, Michele’ );>; Belibasakis, Georgios N <javascript:contributorCitation( ’Belibasakis, Georgios N’);>; Hämmerle, Christoph H F <javascript:contributorCitation( ’Hämmerle, Christoph H F’ );>; Jung,
Ronald E <javascript:contributorCitation( ’Jung, Ronald E’ );>; Teles, Flavia R F<javascript:contributorCitation( ’Teles, Flavia R F’ );>
DOI: https://doi.org/10.1111/jcpe.12788
Posted at the Zurich Open Repository and Archive, University of ZurichZORA URL: https://doi.org/10.5167/uzh-142275Journal ArticleAccepted Version
Originally published at:Sanz-Martin, Ignacio; Doolittle-Hall, Janet; Teles, Ricardo P; Patel, Michele; Belibasakis, Georgios N;Hämmerle, Christoph H F; Jung, Ronald E; Teles, Flavia R F (2017). Exploring the microbiome ofhealthy and diseased peri-implant sites using Illumina sequencing. Journal of Clinical Periodontology,44(12):1274-1284.DOI: https://doi.org/10.1111/jcpe.12788
DR. IGNACIO SANZ MARTIN (Orcid ID : 0000-0001-7037-1163)
DR. RICARDO TELES (Orcid ID : 0000-0002-4216-2812)
DR. FLAVIA TELES (Orcid ID : 0000-0001-6945-9811)
Article type : Epidemiology (Cohort study or case-control study)
Exploring the microbiome of healthy and diseased peri-implant sites using Illumina Sequencing.
nz-Martin I1, Doolittle-Hall J
2, Teles RP
3, Patel M
4, Belibasakis GN
5, Hämmerle CH
6, Jung
6, Teles FRF
3
1Section of Periodontology, Faculty of Odontology, University Complutense of Madrid, Madrid,
Spain.
2Department of Dental Ecology, University of North Carolina at Chapel Hill School of Dentistry,
NC, USA.
3Department of Periodontology, University of North Carolina at Chapel Hill School of Dentistry,
NC, USA.
4Department of Applied Oral Sciences, The Forsyth Institute, Cambridge, MA, USA.
5Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden.
6Clinic of Fixed and Removable Prosthodontics and Dental Material Science, Center of Dental
Medicine, University of Zürich, Switzerland.
Key words: microbiome; dental implant; peri-implantitis; DNA; periodontal; sequencing
Address for correspondence:
Flavia R. F. Teles
Department of Periodontology
UNC School of Dentistry
Koury Health Sciences Building
385 S. Columbia Street Room 3406
Chapel Hill, NC 27599-7455
ABSTRACT
Aim: To compare the microbiome of healthy (H) and diseased (P) peri-implant sites and
determine the core peri-implant microbiome.
Materials and Methods: Submucosal biofilms from 32 H and 35 P sites were analyzed using
16S rRNA sequencing (MiSeq, Illumina), QIIME and HOMINGS. Differences between groups
were determined using Principal Coordinate Analysis (PCoA), t-tests and Wilcoxon rank sum
test and FDR-adjusted. The peri-implant core microbiome was determined.
Results: PCoA showed partitioning between H and P at all taxonomic levels. Bacteroidetes,
Spirochetes and Synergistetes were higher in P, while Actinobacteria prevailed in H (p<0.05).
Porphyromonas and Treponema were more abundant in P and while Rothia and Neisseria were
higher in H (p<0.05). The core peri-implant microbiome contained Fusobacterium, Parvimonas
and Campylobacter sp. T. denticola and P. gingivalis levels were higher in P, as well as F.
alocis, F fastidiosum and T. maltophilum (p<0.05).
Conclusion: The peri-implantitis microbiome is commensal-depleted and pathogen-enriched,
harboring traditional and new pathogens. The core peri-implant microbiome harbors taxa from
genera often associated with periodontal inflammation.
Clinical Relevance
Scientific rationale for study: there is a need for a better understanding of the microbial etiology
of peri-implant diseases.
Principal findings: peri-implantitis sites were heavily colonized by well-known and newly
proposed pathogens, including as-of-yet uncultivated taxa, while healthy peri-implant sites
harbored more commensal taxa. The peri-implant core microbiome was enriched for
Fusobacterium, Parvimonas and Campylobacter species.
Practical implications: Better characterization of the peri-implant microbiome can improve the
understanding of the etiology of peri-implant diseases.
Conflict of Interest
Dr. Ignacio Sanz Martín received an ITI Scholarship from October 2010 to October 2011. The
other authors report no conflict of interest.
Sources of Funding:
This study was supported in part by an ITI Scholarship (to I.S.M.), by the National Institutes of
Health/National Institute of Dental and Craniofacial Research (R03-DE021742 and R01-
DE024767 to F.R.F.T.) and by a pilot grant from Forsyth’s Center for Discovery at the Host-
Biofilm Interface (to F.R.F.T).
Introduction
The widespread use of implants has led to an increase in the number of cases of biofilm-
mediated peri-implant diseases, particularly peri-implantitis. Although long-term longitudinal
studies indicate that implant therapy presents success rates of 95% - 99% (Moraschini et al.,
2015, Vigolo et al., 2015) recent publications have shown a prevalence of peri-implantitis of at
least 20% (Derks et al., 2016b, Derks et al., 2016a, Derks & Tomasi, 2015, Mombelli et al.,
2012, Derks & Tomasi, 2014). It has been suggested that peri-implantitis progresses in non-
linear patterns, and for the majority of cases, the onset occurs within 3 years of function (Derks
et al. 2016b). Peri-implantitis treatment is further complicated by the limited knowledge of its
microbial etiology. While several studies have shown microbial similarities between
periodontitis and peri-implantitis (Carcuac et al., 2016, Charalampakis & Belibasakis, 2015,
Mombelli & Decaillet, 2011), others have implicated species not traditionally associated with
periodontal/peri-implant diseases, such as Helicobacter pylori, Haemophilus influenzae,
Staphylococcus aureus and Staphylococcus anaerobius in the etiology of peri-implantitis
(Persson & Renvert 2014). The current poor understanding of the microbial etiology and
pathogenesis of peri-implantitis may help explain the lack of effective treatment.
Most of the publications that investigated the microbial profiles of peri-implantitis employed
close-ended molecular approaches, which preclude the identification of potentially relevant taxa
that are not targeted by the technique. The use of 16S rRNA Illumina sequencing can overcome
this limitation by allowing an open-ended characterization of the microbiome under study
(Caporaso et al., 2012, Frey et al., 2014, Smith & Peay, 2014), at a coverage depth 100 times
greater than pyrosequencing, with a lower error rate and generating well over 10 times as many
reads as 454 GS FLX (Nelson et al., 2014). Because it combines higher sequence quality at
significantly lower cost per sequence, it has become the leading sequencing platform for human
microbiome sequencing studies (Amarasekara et al., 2015). One limitation of 16S rRNA
sequencing is that certain taxa cannot be distinguished with species-level taxonomic resolution
when the commonly employed QIIME pipeline is used for the downstream taxonomic
classification (Baker et al., 2003, Chakravorty et al., 2007, Ong et al., 2013, Pei et al., 2010,
Wang & Qian, 2009). To manage this limitation, we analyzed the reads generated by MiSeq with
HOMINGS in order to obtain species-level data. HOMINGS is an in silico probe-based platform
that can detect more than 600 bacterial species from 16S rRNA reads (Belstrom et al., 2016a).
The objective of the present investigation was to compare the microbiome of healthy (H) and
diseased (P) peri-implant sites using 16S rRNA Illumina sequencing and HOMINGS and to
determine the peri-implant core microbiome.
Materials and Methods
Patient recruitment
This study was approved by the Ethical Review Committee of the Canton of Zürich, Switzerland
(KEK-Nr: 2011-0159), and was conducted in accordance with the guidelines of the world
Medical Association Declaration of Helsinki. Peri-implantitis patients were recruited as
outpatients referred by private practitioners for the diagnosis and treatment of peri-implant
disease at the Interdisciplinary Peri-implantitis Unit, in the Center of Dental Medicine at the
University of Zürich. Patients presenting successful implants were recruited from the
maintenance clinic at the same institution. Potential participants were informed about the aims of
the study and were assured that their participation was voluntary. All participants provided
written informed consent. The inclusion criteria were good medical health as evidenced by the
medical history, being at least 18 years old and willing to participate in the study. Exclusion
criteria were as follows: periodontal or peri-implant treatment within the past 12 months,
systemic antibiotics use within the past 6 months, pregnancy or lactation, and heavy smoking
(>20 cigarettes/day).
Patients allocated to the peri-implantitis group presented at least one implant with post-insertion
(i.e. at least one-year after loading) radiographic marginal bone loss of at least 2.0 mm mesially
or distally, with concomitant bleeding on probing, according to the definitions presented in the
European Workshop on Periodontology (Zitzmann & Berglundh, 2008). Radiographic bone
levels were recorded by measuring the distance from the implant shoulder to the first visible
bone to implant contact at the mesial and distal aspect of each implant using periapical
radiographs.
The successful implants group included implants with healthy surrounding soft and hard tissues,
determined by absence of pus and detectable radiographic bone loss, and functional loading for
at least one year. Gender and age were recorded, as well as plaque index (PI), bleeding on
probing (BOP), suppuration (SUP), probing pocket depth (PPD), clinical bone loss (BL in mm),
width of keratinized mucosa (KM), implant wear time, time since last check up, implant system
used and nature of reconstruction (single implant, fixed or removable). The clinical parameters
were measured at 6 sites per implant (mesio-, mid-, disto-buccal and mesio-, mid-, disto-
lingual/palatal; except for KM, where the palatal sites were not measured).
Submucosal biofilm sample collection and nucleic acid isolation
Submucosal biofilm samples were collected from peri-implantitis sites (P) in peri-implantitis
patients and from healthy peri-implant sites (H) of participants presenting successful implants. If
multiple implants were present in a patient, one single implant was randomly selected for
sampling. Samples were obtained from the site with the deepest PPD. Prior to sampling, the
supramucosal areas of the implant and supra-structure were isolated using cotton rolls, air-dried
and had the supramucosal biofilm removed. Submucosal biofilm samples were obtained with
sterile Gracey curettes (Deppeler, Rolle, Switzerland). The sample was immediately placed in a
micro-centrifuge tube containing 0.1 ml of RNAse-free TE buffer (10 mM Tris-HCl, 1 mM
EDTA, pH 7.6) and stored at -80°C until analysis.
Bacterial nucleic acids were isolated using the Masterpure DNA purification kit (Epicentre,
Madison, WI, USA), preceded by an overnight incubation with lysozyme at 37°C. DNA quality
and amount were determined using a spectrophotometer and the Picogreen dsDNA quantification
assay (Invitrogen, Carlsbad, CA, USA).
16S rRNA gene sequencing with Illumina sequencing
Sample DNA was analyzed by sequencing the 16S rRNA gene V3-V4 hypervariable region
using MiSeq (Illumina, CA), according to the protocol described by (Caporaso et al., 2011). In
brief, 10-50 ng of DNA were PCR-amplified using the 341F/806R universal primers targeting
the V3-V4 hypervariable region: 341F (forward)
AATGATACGGCGACCACCGAGATCTACACTATGGTAATTGTCCTACGGGAGGCAGC
AG; 806R (reverse) CAAGCAGAAGACGGCATACGAGATTCCCTTGTCTCC
AGTCAGTCAGCCGGACTACHVGGGTWTCTAAT, where the 'TCCCTTGTCTCC' region
represents the appropriate barcode sequences and the underlined bases make the PCR products
Illumina sequencing compatible. PCR samples were purified using AMPure beads and 100 ng of
each barcoded library were pooled, purified and quantified using a bioanalyzer and qPCR. Then,
12 pM of each library mixture spiked with 20% PhiX was loaded onto the MiSeq and sequenced.
Samples that presented poor performance in the pre-sequencing PCR amplification step were
amplified by multiple displacement amplification (Teles et al., 2007) using the Illustra
GenomiPhi V2 DNA Amplification kit (GE Healthcare, USA). Quality control of the reads was
performed using FastQC. The paired end reads were merged using Flash.
Taxonomic assignment
The reads generated using MiSeq were analyzed using the QIIME pipeline (Caporaso et al.
2010). In brief, the paired-end reads were merged using Flash. The libraries were split in QIIME
according to the barcodes used in the sequencing run and low-quality reads were filtered out and
chimeras were removed using UCHIME. Operational taxonomic units (OTUs) were picked using
the Human Oral Microbiome Database (HOMD) v13.2 as the reference database (Chen et al.
2010) using a 97% similarity threshold. Taxonomy was assigned using the Ribosomal Database
Project (RDP) classifier trained on the HOMD v13.2 database with assignments required to meet
a >80% confidence threshold. The phylum and genus-level analyses were performed using
QIIME.
For species-level analyses only, we employed HOMINGS, an in silico 16S rDNA probe analysis
that allows for species-level identification of sequencing datasets generated with MiSeq
(http://homings.forsyth.org) (Gomes et al., 2015). Species-specific, 16S rRNA-based
oligonucleotide “probes” were used in a Perl program based on text string search to identify the
frequency of oral bacterial targets. HOMINGS comprises 671 oligonucleotide probes of 17 to 40
bases that target 538 individual oral bacterial species/phylotypes or, in some cases, a few closely-
related taxa. (Belstrom et al., 2016a, Belstrom et al., 2016d).
Statistical analysis
The demographic and clinical characteristics of the study population were analyzed using the
Fisher’s Exact Test or Student’s t-test. Effects of disease status on the peri-implant microbiome
were examined using Principal Coordinate Analysis (PCoA).
Significant differences in relative abundance (%) between the clinical groups were determined at
the phylum, genus, and species levels. Raw read counts were rarefied to the minimum number of
aligned reads. Only taxa that had at least 3 reads in at least 3 samples in both clinical groups after
rarefication were considered. Significant differences between the healthy and peri-implantitis
groups were determined by Wilcoxon rank sum test with FDR-adjusted p-values <0.05. This
rarefication and determination of significance was repeated 100 times. Taxa that were significant
in 95% of these iterations were further considered in the analyses. Interactions between those
taxa and smoking, as well as implant type were assessed using ANOVA.
The core microbiome of the dataset was determined based on taxa present with ≥0.1% relative
abundance in ≥50% of all samples, as determined by HOMINGS (Abusleme et al., 2013). It was
subdivided into 4 groups based on the taxa mean relative abundance in samples in each clinical
category. Taxa that were present in ≥50% of samples in either the H or P groups, but were not
part of the core microbiome considering all samples, constituted the healthy or peri-implantitis
core microbiomes, respectively.
Results
Demographic and clinical characteristics of the subjects
Eighty-two patients contributing with one implant per patient were initially included in the
investigation. Fifteen patients were excluded due to issues with microbial sampling. Finally, the
analysis of demographic and clinical data from the sampled population showed that the groups
comprising healthy (H, N=32) and diseased peri-implant sites (P, N=35) were well-balanced for
age, gender, implant wear, implant location and type of restoration (Table 1). Non-smokers and
smokers comprised 37.1% and 42.9% of the P group, respectively, whereas in the H group these
were 71.9% and 21.9%, respectively (p=0.03). Straumann was the most frequently used implant
system in both groups (71.9% in H and 47.1% in P), while other systems (i.e., not Straumann or
Branemark) were more common in P (35.3%) than in H (3.1%) (p=0.003). Significant
differences between the groups were observed for the clinical parameters that defined them
(p<0.0001).
Overall microbial sequencing results
Fifteen samples (7 in disease, 8 in health) were excluded from analysis due to the small number
of reads (<5,000). After quality control, chimera depletion and noise filtering, 7,297,772 reads
(median/sample: 114,230; range: 5,055-212,299) were assigned into OTUs (1.7% of them
remained unassigned) and most of them (73.52%) ranged from 408 to 428 bp in length. OTUs
were classified into twelve phyla: Bacteroidetes (25.3%), Proteobacteria (18.4%), Firmicutes
(16.7%), Actinobacteria (15.6%), Fusobacteria (15.6%), Spirochaetes (5.3%), Synergistetes
(0.7%), Tenericutes (0.4%), TM7 (0.3%), SR1 (0.1%), Chloroflexi (0.01%) and GN02 (0.001%).
These OTUs were further classified into 21 classes, 35 orders, 69 families and 94 genera, using
QIIME. HOMINGS identified 85 genera and 210 species using species-specific “probes”. A
complete list of species detected in H and P samples can be found in Supplemental Table 1.
Microbial profiles of healthy and diseased peri-implant sites
Diseased peri-implant sites presented higher diversity, compared to healthy sites (Supplemental
Fig. 1a). Healthy (H) and diseased (P) peri-implant sites presented distinct microbial profiles at
all taxonomic levels (Fig. 1a-c, Supplemental Fig. 1b). Diseased peri-implant sites were
primarily colonized by members of the phyla Bacteroides, Spirochetes and Synergistetes,
whereas healthy peri-implant sites mostly harbored taxa from the Proteobacteria and
Actinobacteria phyla (p<0.05, FDR-adjusted) (Fig. 1a). The genera Porphyromonas (phylum
Bacteroidetes), Treponema (phylum Spirochetes), Filifactor (phylum Firmicutes),
Fretibacterium (phylum Synergistetes and Tannerella (phylum Bacteroidetes) were abundant in
peri-implantitis and were present at a higher relative abundance than those found in the H group
(p<0.05, FDR-adjusted). In contrast, Streptococcus (phylum Firmicutes), Veillonella (phylum
Firmicutes), Rothia (phylum Actinobacteria) and Haemophilus (phylum Proteobacteria) had
higher relative abundance in H sites (p<0.05, FDR-adjusted) (Fig. 1b). Peri-implantitis sites
harbored higher levels of classic pathogens (Fig. 1c), such as Tannerella forsythia, Treponema
denticola and Porphyromonas gingivalis (p<0.05, FDR-adjusted), as well as recently described
new putative pathogens, such as Filifactor alocis, Fretibacterium fastidiosum and Treponema
maltophilum. Implants adjacent to H sites were enriched for Rothia dentocariosa (Fig.1c).
Analysis of the relative abundance of pathogenic and health-compatible species in individual
implants (Fig. 2 a-c) showed that red complex species (T. forsythia, P. gingivalis and T.
denticola) were present at high levels in most P samples compared to H samples (Fig. 2a),
whereas the opposite was observed for species considered compatible with periodontal/peri-
implant health, such as R. dentocariosa, Streptococcus sanguinis and Veillonella dispar (Fig.
2b). Furthermore, newly proposed periodontal pathogens, such as F. alocis, F. fastidiosum,
Eubacterium saphenum and T. maltophilum and as-of-yet uncultured taxa Desulfobulbus sp ot
041, Fretibacterium sp ot 360 and Peptostreptococcacea sp ot 091 and 369 (Fig. 2c) followed a
colonization pattern similar to that of well-recognized periodontal pathogens (T. forsythia, P.
gingivalis and T. denticola) (Fig. 2b).
Next, we employed PCoA to assess the impact of peri-implant health and disease on the local
microbiome. A clear distinction between the microbial composition of H and P implants at all
taxonomic levels was observed (Fig. 3a-c). Even though it was not the primary objective of the
present study, we also explored the potential impact of smoking status and implant system used
on the peri-implant microbiome. When the smoking status was incorporated in the analysis (Fig.
3b), it became apparent that the composition of the microbiome of current smokers in the H
microbiome was closer to that of peri-implantitis cases than to that observed in successful
implants. The impact of the implant system on the local microbiome seemed to be less evident
(Fig. 3c).
Because our results indicated that smoking and implant type could be possible confounders when
the microbiome of peri-implant health and disease was compared, potential interactions between
these parameters were also included in our statistical model. The results revealed an interaction
between smoking and disease status for T. socranskii and Eubacterium saphenum, interactions
between smoking and implant type for T. maltophilum and E. saphenum, and interactions
between disease status and implant type for T. maltophilum.
The commensal and pathogenic microbial profiles of H and P groups became clearer with the
analysis of the peri-implant core microbiome (Fig. 4). Taxa typically known as host-compatible,
such as Veilonella parvula, S. sanguinis and Rothia sp. were part of the peri-implant health core,
while the red complex species (T. forsythia, P. gingivalis and T. denticola), as well as “new
putative pathogens” Filifactor alocis, Treponema maltophilum and Fretibacterium_fastidiosum
comprised the peri-implantitis core.
The use of HOMINGS complemented the QIIME data, as it allowed for the species-level
analysis of taxa that could not be “speciated” by QIIME. For instance, QIIME identified
additional taxa from the genus Porphyromonas (Group_4) in the peri-implantitis core whereas
HOMINGS classified some of them as Porphyromonas endodontalis. Similarly, it identified
Campylobacter gracilis and Veillonella dispar as members of the same core, while QIIME
indicated the presence of members of the genera Campylobacter (phylum Proteobacteria) and
Veillonella (Fig. 4, Supplemental Fig. 2).
Because HOMINGS is a new bioinformatics approach for the analysis of sequencing data, we
compared its results with those obtained with QIIME, a more established pipeline. Since QIIME
does not allow complete species-level taxonomic resolution, genus-level comparisons were
made. Results were quite comparable and most taxa that differed between peri-implant health
and disease reported in Fig. 1a-c were also observed in the QIIME pipeline (Supplementary Fig.
3a-c, Supplemental Table 2).
Discussion
The use of next generation sequencing to explore the microbiome of healthy and diseased peri-
implant sites allowed us to expand the breadth of knowledge of the etiology of this disease. We
used QIIME and HOMINGS to analyze Illumina MiSeq-generated reads and demonstrated that
QIIME and HOMINGS agreed in large part at the genus level (Supplemental Table 2). We
showed that those pipelines should be complementary: to a certain extent QIIME provided
greater breadth of classification whereas HOMINGS provided increased precision. We propose
that using two accepted and complementary techniques will shed more light on a difficult (and
somewhat ill-defined) classification.
In the present study, we were able to determine major microbial differences between peri-
implant healthy and diseased sites and delineate the core microbiome of peri-implant health and
peri-implantitis. The microbial differences between the two clinical groups were clear at all
taxonomic levels. Peri-implantitis sites harbored greater levels of members of the phyla
Bacteroidetes, Spirochetes, Synergistetes and Tenericutes, as well as taxa from the genera
Porphyromonas, Treponema, Filifactor and Fretibacterium. Using fluorescence in situ
hybridization (FISH) combined with epifluorescence microscopy to analyze samples from the
same individuals, Belibasakis and co-workers (Belibasakis et al., 2016) also found elevated
levels of Synergistetes and Spirochetes in peri-implantitis sites. Furthermore, diseased sites were
heavily colonized by traditional pathogens, such as red complex species, as well as newly
proposed pathogenic taxa (Perez-Chaparro et al., 2014), such as F. alocis, F. fastidiosum and
Desulfobulbus sp. oral taxon 041, which is currently uncultured. Conversely, Streptococci were
highly abundant in healthy implants. Our findings corroborate previous studies suggesting
similarities between peri-implant associated microbiota and the periodontal microbiota. Using
checkerboard DNA-DNA hybridization, (Shibli et al., 2008) found high mean counts of all red
complex species in peri-implantitis samples, while host-compatible microorganisms were
reduced. Our results are also in line with early cloning and sequencing studies of the peri-implant
microbiome, in which Porphyromonas, Fusobacterium and Filifactor species were abundant (da
Silva et al., 2014, Koyanagi et al., 2013).
Several of the traditional pathogens as well as newly proposed pathogenic taxa were detected in
overall low mean relative abundance levels in our study (Fig. 1c, 2a, 2c). Albeit, similar to levels
reported by others (Maruyama et al., 2014), the pathogenic capacity of low level taxa might be
intriguing. However, it is well-accepted that the presence of taxa in low abundance does not deny
their potential importance. In fact, that is the tenet of the keystone-pathogen hypothesis, which
holds that “certain low-abundance microbial pathogens can orchestrate inflammatory disease by
remodeling a benign microbiota into a dysbiotic one” (Hajishengallis & Lambris, 2012).
In our peri-implantitis samples, we could not confirm reports of the presence of bacteria typically
detected in infections of implanted medical devices (Mombelli & Decaillet, 2011) or species not
traditionally associated with periodontitis, including Helicobacter pylori, Staphylococcus aureus
and Staphylococcus anaerobius (Persson & Renvert, 2014), although H. influenza was part of the
peri-implant core microbiome. Our results are also in contrast with those of (Kumar et al., 2012).
Using pyrosequencing, the authors found that peri-implantitis harbored lower levels of
Prevotella and Leptotrichia and higher levels of Actinomyces, Peptococcus, Campylobacter,
non-mutans Streptococcus, Butyrivibrio and Streptococcus mutans than healthy implants. A
subsequent paper from the same group (Dabdoub et al., 2013) reported that Staphylococcus was
significantly associated with implant infection and that red complex pathogens were found in
only 37 % of the peri-implantitis biofilms.
The discrepancies presented above may be, in part, due to the use of distinct sample collection
methods. While in our study we employed curettes, several publications on the peri-implant
microbiome have used paper points (Dabdoub et al. 2013, Tsigarida et al. 2015). However, this
method has been demonstrated to harbor DNA of its own (van der Horst et al., 2013), which can
alter the representation of the microbiome under study, particularly when sensitive sequencing
platforms are used. Thus, their use has been discouraged, in favor of curettes (van der Horst et al.,
2013). The above discrepancies may also be due to differences in sequencing platforms and
bioinformatics pipelines. MiSeq Illumina sequencing has recently outperformed pyrosequencing,
allowing an inexpensive and deeper coverage of the microbiome (Caporaso et al., 2012, Frey et
al., 2014, Nelson et al., 2014, Smith & Peay, 2014). MiSeq technology has a lower error rate
compared to pyrosequencing and generates over 10 times as many reads as 454 GS FLX (Nelson
et al., 2014). Thus, it has become the leading sequencing platform, particularly for human
microbiome sequencing studies (Amarasekara et al., 2015). Furthermore, since our goal was to
define a core microbiome commonly present on implants, and those frequently associated with
peri-implant health or disease only, we used a very conservative approach to assign taxa as
present in our samples. For instance, taxa were removed from consideration if they did not have
at least 3 reads in at least 3 samples in both the healthy and peri-implantitis groups after
rarefication. The goal was to “weed out” species belonging to the so-called “rare biosphere”. The
idea of determining taxa consistently found in human disease conditions or in specific
environments, the so-called core microbiome, has been adopted by many in microbial ecology
(Human Microbiome Project Consortium 2012, Backhed et al., 2012, Shade & Handelsman,
2012). The use of this approach might have also contributed to differences between our results
and those from previous studies.
While the healthy and peri-implantitis cores were rich in health-compatible and pathogenic taxa,
respectively, we observed that the peri-implant microbiome core contained members of genera
Fusobacterium, Parvimonas and Campylobacter. Interestingly, those genera harbor species
known to be associated with periodontal inflammation (Socransky & Haffajee, 2005), such as F.
nucleatum, P. micra and C. rectus, all of which are members of the orange complex. Hence, it is
plausible that implants are colonized by bacterial species that predispose the adjacent tissues to
inflammation.
In order obtain species-level taxonomic resolution from MiSeq sequencing, we employed
HOMINGS, an in silico 16S rDNA probe analysis that allows the identification of more than 600
oral bacterial species/phylotypes from MiSeq-generated reads (Belstrom et al., 2016a, Belstrom
et al., 2016d). HOMINGS has been validated and has been increasingly used in oral
microbiology studies (Gomes et al., 2015, McIntyre et al., 2016, Mougeot et al., 2017, Mougeot
et al., 2016, Rudney et al., 2015, Timby et al., 2017, Belstrom et al., 2016a, Belstrom et al.,
2016b, Belstrom et al., 2016c, Belstrom et al., 2016d, Belstrom et al., 2017). Yet, due to the
novelty of the use of HOMINGS, we validated our findings by comparing QIIME and
HOMINGS genus-level data (Supplemental Table 2). The results were quite consistent,
indicating the robustness of our analytical pipelines, and the validity of the HOMINGS
technique.
The combination of these approaches allowed us to detect newly proposed pathogens, such as P.
endododontalis, F. alocis and F. fastidiosum and Desulfobulbus sp oral taxon 041, all with
significant virulence properties. P. endodontalis can induce osteoclastogenesis (Ma et al., 2017,
Yu et al., 2015), F. alocis and P. endodontalis present robust NOD1 and NOD2 stimulatory
activity, respectively (Marchesan et al., 2016) and F. alocis has oxidative stress resistance,
neutrophil and macrophage evasion, adhesion and invasion among its main virulence factors
(Aruni et al., 2014). In addition, transcriptional activity analysis of the periodontal microbiome
and the human host in health and chronic periodontitis showed that the upregulation of bacterial
chemotaxis, flagellar assembly, type III secretion system, and type III CRISPR-Cas system was
driven not only by the red-complex pathogens, but also by candidate pathogens, including F.
alocis and F. fastidiosum (Deng et al., 2017). Finally, our results suggest that phylotypes, such as
Desulfobulbus sp HOT 041, might also contribute to the development of peri-implantitis.
Culture-independent studies have demonstrated the association of this phylotype with
periodontitis (Camelo-Castillo et al., 2015, Oliveira et al., 2016), however information on its
virulence properties is scarce due to its status of “as-of-yet uncultured organism”. Still, the
isolation and sequencing of single cells of oral Desulfobulbus (n = 7) identified genes associated
with several categories of putative virulence factors, including chemotaxis, flagellum
biosynthesis motor proteins secretion, iron acquisition, stress response, evasion, proteases, and
adhesion. Collectively, these findings support the pathogenic role of newly identified pathogens
and that they merit further examination of their potential role as etiologic agents of peri-
implantitis.
The peri-implantitis group included more smokers than the peri-implant healthy group, which is
in line with the literature, as smoking is a well-known risk factor for peri-implantitis (Heitz-
Mayfield, 2008). Part of the deleterious effects of smoking on implant survival might be due to
its impact on the peri-implant microbiota. As demonstrated by (Tsigarida et al., 2015), smoking
shapes the peri-implant microbiome even in clinical health, promoting a pathogen-rich
community depleted of commensals. This phenomenon was also observed in the present study,
where healthy implants from non-smokers had microbial profiles distinct from those found in
current smokers, which appeared to be more similar to a peri-implantitis microbiome (Fig. 3 a-c).
This finding could help justify the differences in the core microbiomes in peri-implant health and
disease. To examine this possibility, our statistical models tested for interactions between
smoking and disease status. We only found interactions between smoking and disease status for
T. socranskii and E. saphenum, thereby supporting the notion that the main driver of differences
in the core microbiome between peri-implant health and peri-implantitis was the disease status.
Due to the scarce literature on the comparison of the microbiomes of different implant systems,
we also explored their impact on the local microbiome. Straumann implants were the most
frequently used in both groups (Table 1), but the implant system did not modulate the microbial
composition of peri-implant biofilms (Fig. 3 b-c).
Although it was not our goal to compare the peri-implant and periodontal microbiota, our results
support the notion that both are similar. Using pyrosequencing, Maruyama and co-workers
(Maruyama et al., 2014) performed this comparison and concluded that the core microbiome
associated with these clinical conditions differed, and suggested that they presented “different
causative pathogens”. However, only Prevotella nigrescens had significantly higher relative
abundance in peri-implantitis compared to periodontitis, while Peptostreptococcaceae [XI] [G-
4], sp. HOT369 and Desulfomicrobium orale were more abundant in periodontitis. Furthermore,
their PCoA did not demonstrate that the two diseases differed in their community structure.
Measures of biodiversity were also similar in peri-implantitis and periodontitis. Our results were
in accordance with theirs that reported that P. gingivalis, T. denticola and T. socranskii were
abundant and prevalent in most samples of peri-implantitis.
The present study suggests that the peri-implant microbiome is quite distinct in health and
disease. Peri-implantitis sites showed an enrichment for pathogens, at the expense of a depletion
of host-compatible species. Well-recognized periodontal pathogens as well as newly proposed
pathogenic taxa, several of which have not yet been cultivated, were associated with peri-implant
disease sites. The core peri-implant microbiome contained members of genera Fusobacterium,
Parvimonas and Campylobacter sp., potentially including pathogenic species such as F.
nucleatum, P. micra and C. rectus. Our findings are clinically relevant, in that based on these
results, it can be postulated that close surveillance, periodic maintenance as well as early
diagnosis of peri-implantitis and immediate intervention are critical for the long-term retention of
implants
Acknowledgements
This study was supported in part by an ITI Fellowship (to I.S.M.), by the National Institutes of
Health/National Institute of Dental and Craniofacial Research (R03-DE021742 and R01-
DE024767 to F.R.F.T.) and by a pilot grant from Forsyth’s Center for Discovery at the Host-
Biofilm Interface (to F.R.F.T). We thank the members of the Forsyth Institute Sequencing Core -
Bruce Paster, Jon Mcafferty, Keerthana Krishnan, Sean Cotton and George Chen - for their
technical assistance. We also thank Dr. Paul Levi for his assistance and support in the initiation
of this project and Dr. Jeff Roach for his technical assistance.
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FIGURE LEGENDS
Figure 1. Box plots of differences in microbial relative abundance between samples from
healthy and peri-implantitis (disease) sites at the level of phylum (a), genus (b), and species
(c). Phylum-, genus- and species-level data were obtained using HOMINGS. Taxa were sorted
according to decreasing relative abundance in subjects with peri-implantitis. Only taxa that were
significantly different between the healthy and peri-implantitis groups (with FDR-adjusted p-
values <0.05) in more than 95% of the 100 iterations performed were plotted.
Figure 2. Relative abundance of pathogenic and health-compatible species in individual
implants. Graphs show mean relative abundance (%) for (a) well-recognized periodontal
pathogens, (b) putative commensals and (c) newly proposed pathogenic taxa. Only species that
were significantly different between the healthy and peri-implantitis groups (with FDR-adjusted
p-values <0.05) in more than 98% of the 100 iterations performed were plotted. Each bar
represents one individual healthy (blue) or diseased (red) implant.
Figure 3. Principal Coordinate Analysis (PCoA) of the microbial composition of samples
according to disease status (a), smoking habit (b) and brand of implant (c). Graphs represent
the PCoA plots of Bray–Curtis distances based on species level data generated with HOMINGS.
Samples from subjects presenting healthy (blue) and diseased (red) peri-implant sites (a) are
plotted according to smoking habit (b) and type of implant (c).
Figure 4. Core Microbiome. Taxa that were present with ≥ 0.1% relative abundance in ≥ 50%
of all samples according to HOMINGS results constitute the core microbiome (blue). Samples
were divided into those representing healthy or peri-implantitis. The core microbiome was
subdivided into 4 groups based on the mean relative abundance of the taxa in samples in each
clinical category. Taxa that were present in ≥ 50% of samples in a single category, but were not
part of the core microbiome considering all samples, constituted the Healthy (green) or Peri-
implantitis (red) core microbiomes. Taxa in those core microbiomes were sub-grouped based on
the mean relative abundance of the taxa in samples in each clinical group. Taxa in bold were
present in ≥ 75% of all samples (Core).
Supplemental Figure 1. Alpha and Beta Diversity. a) Measure of alpha diversity in healthy
and peri-implantitis sites as determined by the Chao 1 index. b) Beta diversity represented by
Principal Component Analysis plots of unweighted Unifrac distances.
Supplemental Figure 2. Core Microbiome. Taxa that were present with ≥ 0.1% relative
abundance in ≥ 50% of all samples according to QIIME results constitute the Core Microbiome
(blue). Samples were divided into those representing healthy or peri-implantitis. The Core
Microbiome was subdivided into 4 groups based on the mean relative abundance of the taxa in
samples in each clinical category. Taxa that were present in ≥ 50% of samples in a single
category, but were not part of the Core Microbiome considering all samples, constitute the
Healthy (green) or Per-iimplantitis (red) Core Microbiomes. Taxa in those Core Microbiomes
were sub-grouped based on the mean relative abundance of the taxa in samples in each clinical
group. Taxa in bold were present in ≥ 75% of all samples (Core). Taxa that could not be resolved
to the species-level are presented as groups, described as follows:
1. Parvimonas_Group_16: Parvimonas_micra, sp_oral_taxon_110, sp_oral_taxon_393
2. Treponema_Group_27: Treponema_socranskii, sp_oral_taxon_268,
sp_oral_taxon_269
3. Veillonella_Group_19: Veillonella_atypica, denticariosi, dispar, parvula
4. Neisseria_Group_21: Neisseria_flava, mucosa, oralis, pharynges, sicca,
sp_oral_taxon_016, sp_oral_taxon_018, subflava
5. Porphyromonas_Group_4: Porphyromonas_endodontalis, sp_oral_taxon_285,
sp_oral_taxon_395
6. Fretibacterium_Group_28: Fretibacterium_sp_oral_taxon_358, sp_oral_taxon_359,
sp_oral_taxon_360, sp_oral_taxon_361, sp_oral_taxon_362, sp_oral_taxon_452,
sp_oral_taxon_453
Supplemental Figure 3. Box plots of differences in microbial relative abundance between
samples from healthy and peri-implantitis sites at the level of phylum (a), genus (b), and
species (c). Phylum, genus and species-level data were obtained using QIIME. Taxa were sorted
according to decreasing relative abundance in subjects with peri-implantitis. Only taxa that were
significantly different between the healthy and peri-implantitis groups (with FDR-adjusted p-
values < 0.05) in more than 95% of the 100 iterations performed were plotted.
Table 1: Characteristics of the peri-implant sites sampled.
Parameters Healthy (n = 32) Peri-implantitis (n = 35) p-value
Patient age (years ± SD) 57 ± 16 59 ± 14 0.509
Gender (M/F) 10/22 12/23 1
Implant wear (years ± SD) 6.3 ± 4.4 7.3 ± 3.6 0.312
Bone loss (mm ± SD) 0.03 ± 0.18 6.7 ± 2.4 <0.0001
Suppuration at any site (# subjects/total) 0/32 18/35 <0.0001
Suppuration proportion of sites per subject 0 0.35 ± 0.41 <0.0001
PD (average of 6 sites) (mm ± SD) 3.1 ± 0.5 7.0 ± 2.5 <0.0001
PD (at site sampled) (mm ± SD) 3.2 ± 0.7 7.5 ± 3.0 <0.0001
BOP at any site (# subjects/total) 19/32 34/35 0.0002
BOP proportion of sites per subject 24 ± 29 80 ± 26 <0.0001
PI at any site (# subjects/total) 13/32 24/35 0.028
PI proportion of sites per subject 12 ± 19 45 ± 38 <0.0001
Keratinized mucosa (average of 5 sites) (mm ± SD) 3.3 ± 1.5 2.2 ± 1.8 0.009
Keratinized mucosa (at site sampled) (mm ± SD) 3.3 ± 2.1 2.3 ± 2.0 0.034
Location of implant (maxilla/mandible) 16/16 18/17 1
Location of implant (posterior/anterior) 24/8 29/6 0.551
Type of restoration (fixed/removable) 30/2 32/3 1
Smokers (%) 0.033
non-smoker 71.9% 37.1%
former smoker 3.1% 11.4%
current smoker 21.9% 42.9%
missing data 3.1% 8.6%
Pack years (years ± SD) 29 ± 22 (n=7) 19 ± 14 (n=15) 0.247
Implant System (%) 0.003
Straumann 71.9% 47.1%
Branemark 25.0% 17.7%
Other 3.1% 35.3%
Note: PD=pocket depth, BOP=bleeding on probing, PI=plaque index, SD=standard deviation.